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首页> 外文期刊>IEEE Transactions on Biomedical Engineering >Bipolar Intracardiac Electrogram Active Interval Extraction During Atrial Fibrillation
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Bipolar Intracardiac Electrogram Active Interval Extraction During Atrial Fibrillation

机译:心房颤动期间双极心内电图主动间隔提取

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摘要

Objective: We introduce novel methods to identify the active intervals (AIs) of intracardiac electrograms (IEGMs) during complex arrhythmias, such as atrial fibrillation (AF). Methods: We formulate the AI extraction problem, which consists of estimating the beginning and duration of the AIs, as a sequence of hypothesis tests. In each test, we compare the variance of a small portion of the bipolar IEGM with its adjacent segments. We propose modified general-likelihood ratio (MGLR) and separating-function-estimation tests; we derive five test statistics (TSs), and show that the AIs can be obtained by threshold crossing the TSs. We apply the proposed methods to the IEGM segments collected from the left atrium of 16 patients (62.4 8.2-years old, four females, four paroxysmal, and twelve persistent AF) prior to catheter ablation. The accuracy of our methods is evaluated by comparing them with previously developed methods and manual annotation (MA). Results: Our results show a high level of similarity between the AIs of the proposed methods and MA, e.g., the true and false positive rates of one of the MGLR-based methods were, respectively, 97.8% and 1.4%. The mean absolute error from estimation of the onset and end of AIs and also for the estimation of the mean cycle length for that approach was 8.7 10.5, 13 15.5, and 4.2 9.4 ms, respectively. Conclusion: The proposed methods can accurately identify onset and duration of AI of the IEGM during AF. Significance: The proposed methods can be used for real-time automated analysis of AF, the most challenging complex arrhythmia.
机译:目的:我们引入新颖的方法来识别复杂心律不齐期间的心内电描记图(IEGM)的活动间隔(AIs),例如房颤(AF)。方法:我们提出了AI提取问题,其中包括估计AI的开始和持续时间,作为一系列假设检验。在每个测试中,我们将双极IEGM的一小部分与其相邻部分的方差进行比较。我们提出了修正的一般似然比(MGLR)和分离函数估计检验。我们得出了五个测试统计量(TS),并表明可以通过跨TS的阈值获得AI。我们将建议的方法应用于从16例患者(62.4 8.2岁,4名女性,4例阵发性和12例持续性AF)的左心房收集的IEGM导管,然后进行消融。通过将它们与以前开发的方法和手动注释(MA)进行比较来评估我们方法的准确性。结果:我们的结果表明,所提出方法的AI与MA之间的相似度很高,例如,一种基于MGLR的方法的真假阳性率分别为97.8%和1.4%。从AI的开始和结束的估计以及该方法的平均周期长度的估计得出的平均绝对误差分别为8.7 10.5、13 15.5和4.2 9.4 ms。结论:所提出的方法可以准确识别房颤期间IEGM的AI发作和持续时间。启示:所提出的方法可用于对最具挑战性的复杂心律失常的AF进行实时自动化分析。

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